A comparison of foot arch measurement reliability using both digital photography and calliper methods
Bibliographic record
Abstract
BACKGROUND: Both calliper devices and digital photographic methods have been used to quantify foot arch height parameters. The purpose of this study was to compare the reliability of both a calliper device and digital photographic method in determining the arch height index (AHI). METHODS: Twenty subjects underwent measurements of AHI on two separate days. On each day, AHI measurements during both sitting and standing were taken using the AHIMS and digital photographic methods by the same single tester. The intra-tester reliability of each measurement technique was assessed using intraclass correlation coefficients (ICC) and standard error of measurement (SEM). Additionally, the relationship between AHI measurements derived from the two different methods was assessed using a correlation analysis. RESULTS: The reliability for both the AHIMS and digital photographic methods was excellent with ICC values exceeding 0.86 and SEM values of less than 0.009 for the AHI. Moreover, the reliability of both measurement techniques was equivalent. There was a strong positive correlation between the AHI values collected using both methods. AHI values calculated using the digital photographic method tended to be greater than those derived using the AHIMS. CONCLUSION: Digital photographic methods offer equivalent intra-tester reliability to previously established calliper methods when assessing AHI. While AHI measurements calculated using both methods were highly related, the greater AHI values in the photographic method implied caution should be exercised when comparing absolute values between the two methods. Future studies are required to determine whether digital photographic methods can be developed with improved validity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".